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News Release 15-138

Establishing a brain trust for data science

Awards for Big Data Regional Innovation Hubs create consortia to catalyze multi-sector partnerships

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US map showing a forecast of Hurricane Joaquin path

Visualization of the ADCIRC Surge Guidance System (ASGS) output for Hurricane Joaquin (2015), advisory number 12, when the storm was considered a potential threat to the North Carolina coast. This ASGS model grid has very high resolution in the North Carolina area, and runs on RENCI high-performance computing systems. Different instances of the forecast system focus on different parts of the US and Gulf of Mexico coasts. The colors show water levels (in feet), with darker reds and oranges depicting higher water levels. The storm's forecast track is also shown, along with the cone of track uncertainty. The simulation is forced by surface winds and atmospheric pressure derived from the National Hurricane Center's consensus forecast track, and the model output is posted to the Nc-Cera.Renci.Org website for dissemination.

Credit: RENCI


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map of United States with regions sectioned out

The Big Data Regional Innovation Hubs cover all 50 states and include commitments from more than 250 organizations--from universities and cities to foundations and Fortune 500 corporations--with the ability to expand further over time.

Credit: NSF


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Researchers inside the Center for Computational Research

Researchers inside the Center for Computational Research, a supercomputing facility supported, in part, by the National Science Foundation at the University at Buffalo, a Big Data Regional Innovation Hub partner.

Credit: Douglas Levere, University at Buffalo


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New York City map estimating total annual building energy consumption at the block and lot level

The map represents an estimate of the total annual building energy consumption at the block level and at the tax lot level for New York City, and is expressed in kilowatt hours (kWh) per square meter of land area. A mathematical model based on statistics, not individual building data, was used to estimate the annual energy consumption values for buildings throughout the five boroughs. Learn more: sel-columbia.github.io/nycenergy/.

Credit: Map created by Shaky Sherpa of Sustainable Engineering Lab (formerly Modi Research Group) Data Source: Spatial distribution of urban building energy consumption by end use B. Howard, L. Parshall, J. Thompson, S. Hammer, J. Dickinson, V. Modi


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heatmaps showing relationships between four underlying measures of student performance and learning

The LearnSphere project led by Carnegie Mellon University seeks to improve educational outcomes by upgrading the infrastructure for educational data mining. Summarizing data from 8,341 students doing online math problems involving 2,400 skills, these heatmaps illustrate relationships between four underlying measures of student performance and learning: prior knowledge, learning rate, guess rate and slip rate. These measures are used to identify student strengths and weaknesses so educators and educational technology can more efficiently personalize the learning experience for students.

Credit: Steven Ritter, Carnegie Learning


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data to knowledge to action conference logo

NSF is a leader in the National Big Data Research and Development Initiative. NSF has made significant investments to advance big data--the collection and analysis of extremely large datasets--by developing foundational techniques and technologies, building infrastructure, and nurturing research and education communities.

Credit: NSF


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